1 Need for a good control

  • A good control group is crucial.

  • To assess the effect of an intervention we need to compare a test and control group

  • This is often not possible in a pretest/posttest design: e.g. effect before and after administring a drug without the use of a placebo group

  • Groups in an observational study are often not comparable: advanced statistical methods are required to draw causal conclusions.

  • Double blinding

  • We have to be aware of confounding!

  • Randomized studies: random assignment of subjects in the study to the different treatment arms \(\rightarrow\) comparable groups.

2 Randomisation

  • Randomisation completely at random (no systematic allocation)

2.1 Simple randomisation

  • Can lead to differences in the number of experimental units in each treatment arm

  • in 5% of the cases we might observe an imbalance of
    • of at least 60:40 in a study with 100 subjects
    • of at least 531:469 in a study with 1000 subjects
  • This imbalance is not problematic but causes a loss in precision.

2.2 Balanced randomisation

  • Equal numbers of each treatment are assigned to a block of 2 or 4 patients.
      1. AB, (2) BA
      1. AABB, (2) ABAB, (3) ABBA, (4) BABA, (5) BAAB, (6) BBAA
  • Gebalanced randomisation ensures \(\pm\) the same number of people in the control and the treatment arm of the experiment.

  • Does not make that we have an equal number of males with and without the treatment, etc.

  • In small studies it is possible that the groups are inbalanced in other characteristics (e.g. gender, race, age …)

  • This is not problematic because it occurs at random, but, again it causes a loss in precision.

2.3 Stratified randomization**

  • The imbalance according to for instance gender can be avoided using stratified randomisation: balanced randomisatie per stratum
Stratified Randomisatie

Stratified Randomisatie

3 Blocking

3.1 Gene expression example

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep

  • dm: diabetic medium, nd: non diabetic medium, co: control
  • 4 bio-reps, 2 techreps/biorep, 2 plates A & B
  • treatment and plate almost entirely confounded

3.2 Nature methods: Points of significance - Blocking

4 Sample size

  • The sample size and the design are crucial

  • The larger the sample size the more precise the results

5 Wrap-up

  • Sample size is very important

  • To assess the effect of a treatment we should compare comparable and representative groups of subjects with and without the treatment (a good control!).

  • In observational studies the researcher cannot choose the treatment. It was the patient or their MD who had chosen it

  • In experimental studies the researcher assigns the treatment

  • Confounding can be avoided via randomisation

  • We can also correct for confounding in the statistical analysis for the confounders that have been registered.

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KSBBQiwgKDIpIEJBCiAgICAtICgxKSBBQUJCLCAoMikgQUJBQiwgKDMpIEFCQkEsICg0KSBCQUJBLCAoNSkgQkFBQiwgKDYpIEJCQUEKCi0gR2ViYWxhbmNlZCByYW5kb21pc2F0aW9uIGVuc3VyZXMgJFxwbSQgdGhlIHNhbWUgbnVtYmVyIG9mIHBlb3BsZSBpbiB0aGUgY29udHJvbCBhbmQgdGhlIHRyZWF0bWVudCBhcm0gb2YgdGhlIGV4cGVyaW1lbnQuIAoKLSBEb2VzIG5vdCBtYWtlIHRoYXQgd2UgaGF2ZSBhbiBlcXVhbCBudW1iZXIgb2YgbWFsZXMgd2l0aCBhbmQgd2l0aG91dCB0aGUgdHJlYXRtZW50LCBldGMuICAKCi0gSW4gc21hbGwgc3R1ZGllcyBpdCBpcyBwb3NzaWJsZSB0aGF0IHRoZSBncm91cHMgYXJlIGluYmFsYW5jZWQgaW4gb3RoZXIgY2hhcmFjdGVyaXN0aWNzIChlLmcuIGdlbmRlciwgcmFjZSwgYWdlIC4uLikKCi0gVGhpcyBpcyBub3QgcHJvYmxlbWF0aWMgYmVjYXVzZSBpdCBvY2N1cnMgYXQgcmFuZG9tLCBidXQsIGFnYWluIGl0IGNhdXNlcyBhIGxvc3MgaW4gcHJlY2lzaW9uLiAKCiMjIFN0cmF0aWZpZWQgcmFuZG9taXphdGlvbioqCgotIFRoZSBpbWJhbGFuY2UgYWNjb3JkaW5nIHRvIGZvciBpbnN0YW5jZSBnZW5kZXIgY2FuIGJlIGF2b2lkZWQgdXNpbmcgc3RyYXRpZmllZCByYW5kb21pc2F0aW9uOiBiYWxhbmNlZCByYW5kb21pc2F0aWUgcGVyIHN0cmF0dW0KCiFbU3RyYXRpZmllZCBSYW5kb21pc2F0aWVdKGh0dHBzOi8vcmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbS9HVFBCL1BTTFMyMC9naC1wYWdlcy9hc3NldHMvZmlncy9zdHJhdGlmaWNhdGlvbi5wbmcpeyB3aWR0aD01MCUgfQoKIyBCbG9ja2luZwoKIyNHZW5lIGV4cHJlc3Npb24gZXhhbXBsZQoKLSBkbTogZGlhYmV0aWMgbWVkaXVtLCBuZDogbm9uIGRpYWJldGljIG1lZGl1bSwgY286IGNvbnRyb2wKLSA0IGJpby1yZXBzLCAyIHRlY2hyZXBzL2Jpb3JlcAohW10oaHR0cHM6Ly9yYXcuZ2l0aHVidXNlcmNvbnRlbnQuY29tL0dUUEIvUFNMUzIwL2doLXBhZ2VzL2Fzc2V0cy9maWdzL3FwY3JCYWREZXNpZ24xLnBuZyl7IHdpZHRoPTEwMCUgfQoKLSBkbTogZGlhYmV0aWMgbWVkaXVtLCBuZDogbm9uIGRpYWJldGljIG1lZGl1bSwgY286IGNvbnRyb2wKLSA0IGJpby1yZXBzLCAyIHRlY2hyZXBzL2Jpb3JlcCwgMiBwbGF0ZXMgQSAmIEIKLSB0cmVhdG1lbnQgYW5kIHBsYXRlIGFsbW9zdCBlbnRpcmVseSBjb25mb3VuZGVkIAoKIVtdKGh0dHBzOi8vcmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbS9HVFBCL1BTTFMyMC9naC1wYWdlcy9hc3NldHMvZmlncy9xcGNyQmFkRGVzaWduMi5wbmcpeyB3aWR0aD0xMDAlIH0KCiMjIE5hdHVyZSBtZXRob2RzOiBQb2ludHMgb2Ygc2lnbmlmaWNhbmNlIC0gQmxvY2tpbmcgCgohW10oaHR0cHM6Ly93d3cubmF0dXJlLmNvbS9hcnRpY2xlcy9ubWV0aC4zMDA1LnBkZil7IHdpZHRoPTEwMCUgfQoKCgojIFNhbXBsZSBzaXplIAoKLSBUaGUgc2FtcGxlIHNpemUgYW5kIHRoZSBkZXNpZ24gYXJlIGNydWNpYWwgCgotIFRoZSBsYXJnZXIgdGhlIHNhbXBsZSBzaXplIHRoZSBtb3JlIHByZWNpc2UgdGhlIHJlc3VsdHMKCgojIFdyYXAtdXAKCi0gU2FtcGxlIHNpemUgaXMgdmVyeSBpbXBvcnRhbnQKCi0gVG8gYXNzZXNzIHRoZSBlZmZlY3Qgb2YgYSB0cmVhdG1lbnQgd2Ugc2hvdWxkIGNvbXBhcmUgY29tcGFyYWJsZSBhbmQgcmVwcmVzZW50YXRpdmUgZ3JvdXBzIG9mIHN1YmplY3RzIHdpdGggYW5kIHdpdGhvdXQgdGhlIHRyZWF0bWVudCAoYSBnb29kIGNvbnRyb2whKS4gCgotIEluIG9ic2VydmF0aW9uYWwgc3R1ZGllcyB0aGUgcmVzZWFyY2hlciBjYW5ub3QgY2hvb3NlIHRoZSB0cmVhdG1lbnQuIEl0IHdhcyB0aGUgcGF0aWVudCBvciB0aGVpciBNRCB3aG8gaGFkIGNob3NlbiBpdCAKCi0gSW4gZXhwZXJpbWVudGFsIHN0dWRpZXMgdGhlIHJlc2VhcmNoZXIgYXNzaWducyB0aGUgdHJlYXRtZW50IAoKLSBDb25mb3VuZGluZyBjYW4gYmUgYXZvaWRlZCB2aWEgcmFuZG9taXNhdGlvbiAKCi0gV2UgY2FuIGFsc28gY29ycmVjdCBmb3IgY29uZm91bmRpbmcgaW4gdGhlIHN0YXRpc3RpY2FsIGFuYWx5c2lzIGZvciB0aGUgY29uZm91bmRlcnMgdGhhdCBoYXZlIGJlZW4gcmVnaXN0ZXJlZC4gCgo=